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  • Open Access

    ARTICLE

    A Multi-Level Circulant Cross-Modal Transformer for Multimodal Speech Emotion Recognition

    Peizhu Gong1, Jin Liu1, Zhongdai Wu2, Bing Han2, Y. Ken Wang3, Huihua He4,*

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4203-4220, 2023, DOI:10.32604/cmc.2023.028291

    Abstract Speech emotion recognition, as an important component of human-computer interaction technology, has received increasing attention. Recent studies have treated emotion recognition of speech signals as a multimodal task, due to its inclusion of the semantic features of two different modalities, i.e., audio and text. However, existing methods often fail in effectively represent features and capture correlations. This paper presents a multi-level circulant cross-modal Transformer (MLCCT) for multimodal speech emotion recognition. The proposed model can be divided into three steps, feature extraction, interaction and fusion. Self-supervised embedding models are introduced for feature extraction, which give a more powerful representation of the… More >

  • Open Access

    ARTICLE

    Image Color Rendering Based on Hinge-Cross-Entropy GAN in Internet of Medical Things

    Hong’an Li1, Min Zhang1,*, Dufeng Chen2, Jing Zhang1, Meng Yang3, Zhanli Li1

    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 779-794, 2023, DOI:10.32604/cmes.2022.022369

    Abstract Computer-aided diagnosis based on image color rendering promotes medical image analysis and doctor-patient communication by highlighting important information of medical diagnosis. To overcome the limitations of the color rendering method based on deep learning, such as poor model stability, poor rendering quality, fuzzy boundaries and crossed color boundaries, we propose a novel hinge-cross-entropy generative adversarial network (HCEGAN). The self-attention mechanism was added and improved to focus on the important information of the image. And the hinge-cross-entropy loss function was used to stabilize the training process of GAN models. In this study, we implement the HCEGAN model for image color rendering… More > Graphic Abstract

    Image Color Rendering Based on Hinge-Cross-Entropy GAN in Internet of Medical Things

  • Open Access

    ARTICLE

    Vehicle Density Prediction in Low Quality Videos with Transformer Timeseries Prediction Model (TTPM)

    D. Suvitha*, M. Vijayalakshmi

    Computer Systems Science and Engineering, Vol.44, No.1, pp. 873-894, 2023, DOI:10.32604/csse.2023.025189

    Abstract Recent advancement in low-cost cameras has facilitated surveillance in various developing towns in India. The video obtained from such surveillance are of low quality. Still counting vehicles from such videos are necessity to avoid traffic congestion and allows drivers to plan their routes more precisely. On the other hand, detecting vehicles from such low quality videos are highly challenging with vision based methodologies. In this research a meticulous attempt is made to access low-quality videos to describe traffic in Salem town in India, which is mostly an un-attempted entity by most available sources. In this work profound Detection Transformer (DETR)… More >

  • Open Access

    ARTICLE

    An Innovative Approach Utilizing Binary-View Transformer for Speech Recognition Task

    Muhammad Babar Kamal1, Arfat Ahmad Khan2, Faizan Ahmed Khan3, Malik Muhammad Ali Shahid4, Chitapong Wechtaisong2,*, Muhammad Daud Kamal5, Muhammad Junaid Ali6, Peerapong Uthansakul2

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5547-5562, 2022, DOI:10.32604/cmc.2022.024590

    Abstract The deep learning advancements have greatly improved the performance of speech recognition systems, and most recent systems are based on the Recurrent Neural Network (RNN). Overall, the RNN works fine with the small sequence data, but suffers from the gradient vanishing problem in case of large sequence. The transformer networks have neutralized this issue and have shown state-of-the-art results on sequential or speech-related data. Generally, in speech recognition, the input audio is converted into an image using Mel-spectrogram to illustrate frequencies and intensities. The image is classified by the machine learning mechanism to generate a classification transcript. However, the audio… More >

  • Open Access

    ARTICLE

    WMA: A Multi-Scale Self-Attention Feature Extraction Network Based on Weight Sharing for VQA

    Yue Li, Jin Liu*, Shengjie Shang

    Journal on Big Data, Vol.3, No.3, pp. 111-118, 2021, DOI:10.32604/jbd.2021.017169

    Abstract Visual Question Answering (VQA) has attracted extensive research focus and has become a hot topic in deep learning recently. The development of computer vision and natural language processing technology has contributed to the advancement of this research area. Key solutions to improve the performance of VQA system exist in feature extraction, multimodal fusion, and answer prediction modules. There exists an unsolved issue in the popular VQA image feature extraction module that extracts the fine-grained features from objects of different scale difficultly. In this paper, a novel feature extraction network that combines multi-scale convolution and self-attention branches to solve the above… More >

  • Open Access

    ARTICLE

    Chinese Q&A Community Medical Entity Recognition with Character-Level Features and Self-Attention Mechanism

    Pu Han1,2, Mingtao Zhang1, Jin Shi3, Jinming Yang4, Xiaoyan Li5,*

    Intelligent Automation & Soft Computing, Vol.29, No.1, pp. 55-72, 2021, DOI:10.32604/iasc.2021.017021

    Abstract With the rapid development of Internet, the medical Q&A community has become an important channel for people to obtain and share medical and health knowledge. Online medical entity recognition (OMER), as the foundation of medical and health information extraction, has attracted extensive attention of researchers in recent years. In order to further improve the research progress of Chinese OMER, LSTM-Att-Med model is proposed in this paper to capture more external semantic features and important information. First, Word2vec is used to generate the character-level vectors with semantic features on the basis of the unlabeled corpus in the medical domain and open… More >

  • Open Access

    ARTICLE

    Leverage External Knowledge and Self-attention for Chinese Semantic Dependency Graph Parsing

    Dianqing Liu1,2, Lanqiu Zhang1,2, Yanqiu Shao1,2,*, Junzhao Sun3

    Intelligent Automation & Soft Computing, Vol.28, No.2, pp. 447-458, 2021, DOI:10.32604/iasc.2021.016320

    Abstract Chinese semantic dependency graph (CSDG) parsing aims to analyze the semantic relationship between words in a sentence. Since it is a deep semantic analysis task, the parser needs a lot of prior knowledge about the real world to distinguish different semantic roles and determine the range of the head nodes of each word. Existing CSDG parsers usually use part-of-speech (POS) and lexical features, which can only provide linguistic knowledge, but not semantic knowledge about the word. To solve this problem, we propose an entity recognition method based on distant supervision and entity classification to recognize entities in sentences, and then… More >

  • Open Access

    ARTICLE

    Hashtag Recommendation Using LSTM Networks with Self-Attention

    Yatian Shen1, Yan Li1, Jun Sun1,*, Wenke Ding1, Xianjin Shi1, Lei Zhang1, Xiajiong Shen1, Jing He2

    CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1261-1269, 2019, DOI:10.32604/cmc.2019.06104

    Abstract On Twitter, people often use hashtags to mark the subject of a tweet. Tweets have specific themes or content that are easy for people to manage. With the increase in the number of tweets, how to automatically recommend hashtags for tweets has received wide attention. The previous hashtag recommendation methods were to convert the task into a multi-class classification problem. However, these methods can only recommend hashtags that appeared in historical information, and cannot recommend the new ones. In this work, we extend the self-attention mechanism to turn the hashtag recommendation task into a sequence labeling task. To train and… More >

  • Open Access

    ARTICLE

    Keyphrase Generation Based on Self-Attention Mechanism

    Kehua Yang1,*, Yaodong Wang1, Wei Zhang1, Jiqing Yao2, Yuquan Le1

    CMC-Computers, Materials & Continua, Vol.61, No.2, pp. 569-581, 2019, DOI:10.32604/cmc.2019.05952

    Abstract Keyphrase greatly provides summarized and valuable information. This information can help us not only understand text semantics, but also organize and retrieve text content effectively. The task of automatically generating it has received considerable attention in recent decades. From the previous studies, we can see many workable solutions for obtaining keyphrases. One method is to divide the content to be summarized into multiple blocks of text, then we rank and select the most important content. The disadvantage of this method is that it cannot identify keyphrase that does not include in the text, let alone get the real semantic meaning… More >

  • Open Access

    ARTICLE

    Joint Self-Attention Based Neural Networks for Semantic Relation Extraction

    Jun Sun1, Yan Li1, Yatian Shen1,*, Wenke Ding1, Xianjin Shi1, Lei Zhang1, Xiajiong Shen1, Jing He2

    Journal of Information Hiding and Privacy Protection, Vol.1, No.2, pp. 69-75, 2019, DOI:10.32604/jihpp.2019.06357

    Abstract Relation extraction is an important task in NLP community. However, some models often fail in capturing Long-distance dependence on semantics, and the interaction between semantics of two entities is ignored. In this paper, we propose a novel neural network model for semantic relation classification called joint self-attention bi-LSTM (SA-Bi-LSTM) to model the internal structure of the sentence to obtain the importance of each word of the sentence without relying on additional information, and capture Long-distance dependence on semantics. We conduct experiments using the SemEval-2010 Task 8 dataset. Extensive experiments and the results demonstrated that the proposed method is effective against… More >

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